Basic protocols in quantum reinforcement learning with superconducting circuits

Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.

[1]  Hideo Mabuchi,et al.  A coherent perceptron for all-optical learning , 2015, 1501.01608.

[2]  L. DiCarlo,et al.  Digital feedback in superconducting quantum circuits , 2015, 1508.01385.

[3]  H. Neven,et al.  Digitized adiabatic quantum computing with a superconducting circuit. , 2015, Nature.

[4]  Hans-J. Briegel,et al.  Quantum-enhanced machine learning , 2016, Physical review letters.

[5]  Barry C Sanders,et al.  High-Fidelity Single-Shot Toffoli Gate via Quantum Control. , 2015, Physical review letters.

[6]  Alexandre Blais,et al.  Quantum information processing with circuit quantum electrodynamics , 2007 .

[7]  Thierry Paul,et al.  Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[8]  Nicolai Friis,et al.  Coherent controlization using superconducting qubits , 2015, Scientific Reports.

[9]  R. Barends,et al.  Superconducting quantum circuits at the surface code threshold for fault tolerance , 2014, Nature.

[10]  F. Petruccione,et al.  An introduction to quantum machine learning , 2014, Contemporary Physics.

[11]  Jiangfeng Du,et al.  Experimental realization of a quantum support vector machine. , 2015, Physical review letters.

[12]  Enrique Solano,et al.  Advanced-Retarded Differential Equations in Quantum Photonic Systems , 2016, Scientific Reports.

[13]  E. Solano,et al.  Biomimetic Cloning of Quantum Observables , 2013, Scientific Reports.

[14]  E. Solano,et al.  Quantum Memristors with Superconducting Circuits , 2016, Scientific Reports.

[15]  Jacob biamonte,et al.  Quantum machine learning , 2016, Nature.

[16]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[17]  José David Martín-Guerrero,et al.  Quantum Machine Learning without Measurements , 2016, ArXiv.

[18]  Miguel C. Soriano,et al.  Photonic delay systems as machine learning implementations , 2015, J. Mach. Learn. Res..

[19]  Andrew W. Cross,et al.  Experimental Demonstration of a Resonator-Induced Phase Gate in a Multiqubit Circuit-QED System. , 2016, Physical review letters.

[20]  S. Girvin,et al.  Observation of high coherence in Josephson junction qubits measured in a three-dimensional circuit QED architecture. , 2011, Physical review letters.

[21]  E Knill,et al.  Quantum state tomography of an itinerant squeezed microwave field. , 2010, Physical review letters.

[22]  Nicolò Spagnolo,et al.  Learning an unknown transformation via a genetic approach , 2016, Scientific Reports.

[23]  Anna Levit,et al.  Reinforcement learning using quantum Boltzmann machines , 2016, Quantum Inf. Comput..

[24]  D. Ristè,et al.  Digital Feedback Control , 2016 .

[25]  Ujjwal Sen,et al.  Trapped ion chain as a neural network: error resistant quantum computation. , 2007, Physical review letters.

[26]  Vedran Dunjko,et al.  Quantum speedup for active learning agents , 2014, 1401.4997.

[27]  Franco Nori,et al.  Qubit-based memcapacitors and meminductors , 2016, 1602.07230.

[28]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[29]  Enrique Solano,et al.  Artificial Life in Quantum Technologies , 2015, Scientific Reports.

[30]  Mazyar Mirrahimi,et al.  Persistent control of a superconducting qubit by stroboscopic measurement feedback , 2012, 1301.6095.

[31]  Barry C. Sanders,et al.  Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach , 2015, ArXiv.

[32]  G. Wendin.  Quantum information processing with superconducting circuits: a review , 2016, Reports on progress in physics. Physical Society.

[33]  John M. Martinis,et al.  Logic gates at the surface code threshold: Superconducting qubits poised for fault-tolerant quantum computing , 2014 .

[34]  Peter V. E. McClintock,et al.  Quantum aspects of life , 2011 .

[35]  E. Solano,et al.  Quantum memristors , 2015, Scientific Reports.

[36]  C. C. Bultink,et al.  Feedback control of a solid-state qubit using high-fidelity projective measurement. , 2012, Physical review letters.

[37]  G. C. Hilton,et al.  Amplification and squeezing of quantum noise with a tunable Josephson metamaterial , 2008, 0806.0659.

[38]  M. A. Martin-Delgado,et al.  On Quantum Effects in a Theory of Biological Evolution , 2011, Scientific Reports.

[39]  Enrique Solano,et al.  Supervised Quantum Learning without Measurements , 2017, Scientific Reports.

[40]  G. D’Ariano,et al.  Optimal quantum tomography of States, measurements, and transformations. , 2008, Physical review letters.

[41]  Michel Devoret,et al.  Superconducting quantum bits , 2005 .

[42]  Hartmut Neven,et al.  NIPS 2009 Demonstration: Binary Classification using Hardware Implementation of Quantum Annealing , 2009 .

[43]  Rudolf Sollacher,et al.  Quantum pattern recognition with liquid-state nuclear magnetic resonance , 2008, 0802.1592.

[44]  L. DiCarlo,et al.  Deterministic entanglement of superconducting qubits by parity measurement and feedback , 2013, Nature.

[45]  Robert Gardner,et al.  Quantum generalisation of feedforward neural networks , 2016, npj Quantum Information.

[46]  Daniel Brunner,et al.  Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.

[47]  G. D’Ariano,et al.  Optimal quantum learning of a unitary transformation , 2009, 0903.0543.

[48]  C-Y Lu,et al.  Entanglement-based machine learning on a quantum computer. , 2015, Physical review letters.